---
title: "Analysis on Gender Statictics"
output: 
  flexdashboard::flex_dashboard:
    orientation: 
    vertical_layout: fill 
    source_code: embed
---

```{r setup, include=FALSE, message=FALSE, warning=FALSE, echo=FALSE}
knitr::opts_chunk$set(include=TRUE, message=FALSE, warning=FALSE, echo=FALSE)
library(readr)
library(tidyverse)
library(knitr)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(tibble)
library(htmltools)

```

Introduction {data-icon="fa-globe"}
=================================================================================

INTRODUCTION

***
Gender inequality remains a major barrier to human development. Girls and women have made major strides since 1990, but they have not yet gained gender equity. 

Women who want to work have a harder time finding a job than men. This problem is particularly marked in developing countries where the unemployment rate for women is more than that of developed countries.

Hence, the primary analysis of our presentation is to compare and contrast the gender statistic in developed countries like *United States & France* and developing countries like *Colombia and Egypt* from the years 2010 to 2019 on various factors like:

* Comparison of  *inflation, gender distribution for the labor force, and life expectancy* in various countries.

* Gender workflow distribution and  gender workflow comparison in various sectors like agriculture, industry, and service**

*  The analysis on the total unemployment difference between males and females and *analysis on the basis of qualifications in developed and developing countries.*

***

Team Members

|Name	          |Email Id	                  |Student Id|
|---------------|:-------------------------:|----------|
|Hsin Hsu Janice|hhsu0002@student.monash.edu|32195109  |
|Nelson Quintero|nqui0008@student.monash.edu|32254962  |
|Ratul Wadhwa   |rwad0002@student.monash.edu|32055587  |

Inflation {data-icon="ion-arrow-graph-up-right"}
=================================================================================

Row {.sidebar data-width=300}
-------------------------------------

**INFLATION**

These plots are the **inflation rate and the labor force for US, France, Colombia and Egypt**. For the inflation rate, we can see that for the developed countries, such as US and France, both have the pattern that there is a **sharp down happened in 2015**. This is because the crude oil price collapse. 

A number of factors can explain this, such as:

1. the strong US dollar

2. OPEC was unwilling to stabilise the oil price


Column {.tabset data-width=70%} 
---------------------------------------------------------------------------------


```{r echo=FALSE, warning=FALSE, message=FALSE}
# Libraries
library(tidyverse)
library(ggplot2)
library(readr)
library(broom)
library(stringr)
library(patchwork)
library(kableExtra)
library(knitr)
library(bookdown)
library(naniar)
library(GGally)

Genderstatistics <- read_csv("Data/Genderstatistics.csv")%>%
  rename('2011' = '2011 [YR2011]',
         '2012' = '2012 [YR2012]', 
         '2013' = '2013 [YR2013]', 
         '2014' = '2014 [YR2014]', 
         '2015' = '2015 [YR2015]', 
         '2016' = '2016 [YR2016]', 
         '2017' = '2017 [YR2017]', 
         '2018' = '2018 [YR2018]', 
         '2019' = '2019 [YR2019]') %>%
  mutate(`2019` = as.numeric(`2019`)) 
```

```{r echo=FALSE, warning=FALSE, message=FALSE}
analysis<- Genderstatistics %>%
  filter(`Series Name` %in% c("Inflation, consumer prices (annual %)", "Population ages 15-64, female", "Population ages 15-64, male", "Life expectancy at birth, female (years)","Life expectancy at birth, male (years)")) %>%
  select(-c(`Series Code`,`Country Code`)) %>%
   pivot_longer(cols = -c(`Country Name`,`Series Name`),
               names_to = "Year",
               values_to = "count") %>%
  pivot_wider(names_from = "Series Name",
              values_from = "count") 
```

```{r echo=FALSE, warning=FALSE, message=FALSE}
analysis <- analysis %>%
mutate(Inflation = as.numeric(`Inflation, consumer prices (annual %)`)) %>%
  mutate(Population_ages_15_64_female = as.numeric(`Population ages 15-64, female`)) %>%
  mutate(Population_ages_15_64_male = as.numeric(`Population ages 15-64, male`)) %>%
  mutate(labour_force = `Population_ages_15_64_female`+`Population_ages_15_64_male`) %>%
  mutate(Year = as.numeric(Year)) %>%
  mutate(Life_expectancy_at_birth_female = as.numeric(`Life expectancy at birth, female (years)`))%>%
  mutate(Life_expectancy_at_birth_male = as.numeric(`Life expectancy at birth, male (years)`))%>%
  select(-c(`Inflation, consumer prices (annual %)`,`Population ages 15-64, female`,`Population ages 15-64, male`, `Life expectancy at birth, female (years)`, `Life expectancy at birth, male (years)`))
```


```{r A1, fig.cap = "Inflation vs Labour force" , echo=FALSE, warning=FALSE, message=FALSE, fig.height=4}
library(purrr)
library(ggplot2)
library(patchwork)
countries <- c("Colombia", "United States", "France", "Egypt, Arab Rep.")

infla_labour <- function(country){
  p1 <- analysis %>%
  filter(`Country Name`== country)%>%
    na.omit()%>%
    ggplot(aes(x=Year, y=Inflation)) +
  geom_line(color="#69b3a2", size=2) +
  scale_x_continuous(breaks = c(2011:2019))+
    scale_y_continuous(labels = scales::comma)+
  ggtitle("Inflation rate") +
    labs(title = country)
  
   p2 <- analysis %>%
  filter(`Country Name`== country)%>%
     na.omit()%>%
ggplot(aes(x=Year, y=labour_force)) +
  geom_line(color="grey",size=2) +
  scale_x_continuous(breaks = c(2011:2019))+
     scale_y_continuous(labels = scales::comma)+
  ggtitle("number of labour force") +
     labs(title = country)

p1 + p2
}
```


### Colombia

```{r}
infla_labour("Colombia")
```

### Egypt

```{r}

infla_labour("Egypt, Arab Rep.")
```

### United States

```{r , fig.cap = "Inflation vs Labour force" , echo=FALSE, warning=FALSE, message=FALSE, fig.height=4}
infla_labour("United States")

```

### France

```{r}
infla_labour("France")

```

Column {data-width=25%}
-----------------------------------------------------------------------

```{r A2, echo=FALSE, warning=FALSE, message=FALSE}
library(kableExtra)
t1 <- analysis %>%
  select(`Country Name`,Year,Inflation,labour_force) %>%
  filter(Year == "2015") %>%
   knitr::kable(
    caption = "Inflation and the labour force in 2015"
  ) %>%
  kable_styling(c("hover", "striped"), full_width = FALSE, font_size = 20)

t1
```


Gender Distribution {data-icon="ion-ios-color-filter"}
=================================================================================

Row {.sidebar data-width=300}
---------------------------------------------------------------------------------

**GENDER DISTRIBUTION**

According to these plots, the **amounts of female and male labor force are quite equal**, as we can see both lines overlap. Additionally, US has the most labor force, it is just simply due to the big population.


**LIFE EXPECTANCY**

* Life expectancy for both genders. It is really obvious that female lives longer than male.


Row {.tabset}
---------------------------------------------------------------------------------

### Gender Distribution in Labour Force
```{r A3, echo=FALSE, warning=FALSE, message=FALSE}
analysis %>%
  ggplot(aes(x = Year))+
  geom_line(aes(y = Population_ages_15_64_female), color = "red")+
  geom_line(aes(y = Population_ages_15_64_male), color = "green")+
  ylab("Gender distribution for labour force")+
  scale_x_continuous(breaks = c(2011:2019))+
    scale_y_continuous(labels = scales::comma)+
  theme_light()+
  facet_wrap(~`Country Name`)
```

### Life Expectancy for Female and Male at Birth

```{r A4, echo=FALSE, warning=FALSE, message=FALSE, fig.pos="Center"}
analysis %>%
  ggplot(aes(x = Year))+
  geom_line(aes(y = Life_expectancy_at_birth_female), color = "red")+
  geom_line(aes(y = Life_expectancy_at_birth_male), color = "green")+
  ylab("Life expectancy for female and male")+
  scale_x_continuous(breaks = c(2011:2019))+
    scale_y_continuous(labels = scales::comma)+
  facet_wrap(~`Country Name`)+
  theme_light()
```

Employment-Country {data-icon="ion-android-globe"}
=================================================================================

```{r readingdata, message=FALSE, echo=FALSE, warning=FALSE}

readingdata <- read_csv("data/088121e0-ea83-4b15-be2a-1bcc36ea893f_Data.csv")

```


```{r cleaningdata, fig.width=10,fig.height=9, fig.width=12, echo=FALSE, warning=FALSE, message=FALSE}

byyear <- readingdata %>% select(c(`Series Name`,`Country Name`, `2011 [YR2011]`:`2019 [YR2019]`)) %>% 
  filter(`Series Name` %in%  c("Employment in agriculture, female (% of female employment) (modeled ILO estimate)",
                               "Employment in agriculture, male (% of male employment) (modeled ILO estimate)",
                               "Employment in industry, female (% of female employment) (modeled ILO estimate)",
                               "Employment in industry, male (% of male employment) (modeled ILO estimate)",
                               "Employment in services, female (% of female employment) (modeled ILO estimate)",
                               "Employment in services, male (% of male employment) (modeled ILO estimate)")) %>% 
  mutate(`Series Name` = case_when(`Series Name` == "Employment in agriculture, female (% of female employment) (modeled ILO estimate)" ~ "Females in Agriculture (%)",
                                   `Series Name` == "Employment in agriculture, male (% of male employment) (modeled ILO estimate)" ~ "Males in Agriculture (%)",
                                   `Series Name` == "Employment in industry, female (% of female employment) (modeled ILO estimate)" ~ "Females in Industry (%)",
                                   `Series Name` == "Employment in industry, male (% of male employment) (modeled ILO estimate)" ~ "Males in Industry (%)",
                                   `Series Name` == "Employment in services, female (% of female employment) (modeled ILO estimate)" ~ "Females in Services (%)",
                                   `Series Name` == "Employment in services, male (% of male employment) (modeled ILO estimate)" ~ "Males in Services (%)"),
         `Country Name` = case_when(`Country Name` == "Egypt, Arab Rep." ~ "Egypt",
                                    TRUE~`Country Name`),
                                   "2011" = as.double(`2011 [YR2011]`),
                                   "2012" = as.double(`2012 [YR2012]`),
                                   "2013" = as.double(`2013 [YR2013]`),
                                   "2014" = as.double(`2014 [YR2014]`),
                                   "2015" = as.double(`2015 [YR2015]`),
                                   "2016" = as.double(`2016 [YR2016]`), 
                                   "2017" = as.double(`2017 [YR2017]`), 
                                   "2018" = as.double(`2018 [YR2018]`), 
                                   "2019" = as.double(`2019 [YR2019]`)) %>% 
  filter(`Country Name` %in% c("United States", "Egypt","Colombia", "France")) %>% 
  select(`Series Name`, 
         `Country Name`, 
         `2011`:`2019`) %>% 
  pivot_longer(names_to = "Year", 
               values_to = "Percentage", 
               cols =  c(-`Series Name`,
                         -`Country Name`))
```


Row {.sidebar data-width=300}
---------------------------------------------------------------------------------

**Analysis by Gender**

* High income countries such as United States and France and low income such as Colombia and Egypt were taken into account to evaluate the labor force condition and the general trends of the citizens performing jobs in agriculture, industry and services jobs.

* The distribution in the job market according to the gender and country tends to variate according to the economy of each country. 

* High income countries such as United States or France manages a similar trend in every industry according to the gender.

Column
-----------------------------------------------------------------------

### High Income Countries


```{r highinc, fig.width=12,fig.height=6, echo=FALSE, warning=FALSE, message=FALSE}

usafraemp <- byyear %>% 
  filter(`Country Name` %in% c("United States",
                               "France")) %>% 
  ggplot(aes(x = `Year`,
             y = `Percentage`, 
             group = `Series Name`, 
             color = `Series Name`))+
  facet_wrap(~`Country Name`)+
  geom_point(size = 2.5)+
  geom_line(aes(linetype=`Series Name` %in%  c("Females in Agriculture (%)", 
                                               "Females in Industry (%)", 
                                               "Females in Services (%)")),
            size= 1.5, 
            show.legend = F)+
  theme(legend.position = "bottom")+
  labs( title = "Gender Workforce in High Income Countries")+
  scale_y_continuous(labels = function(x) paste0(x*1, "%"), 
                     breaks = seq(0,100,10), 
                     limits = c(0,100))
  
usafraemp

```

Column
----------------------------------------------------------------------- 

### Low Income Countries

```{r lowinc, fig.width=12,fig.height=6, echo=FALSE, warning=FALSE, message=FALSE}

colegyemp <- byyear %>% 
  filter(`Country Name` %in% c("Colombia",
                               "Egypt")) %>% 
  ggplot(aes(x = `Year`,
             y = `Percentage`, 
             group = `Series Name`, 
             color = `Series Name`))+
  facet_wrap(~`Country Name`)+
  geom_point(size = 2.5)+
  geom_line(aes(linetype=`Series Name` %in%  c("Females in Agriculture (%)", 
                                               "Females in Industry (%)", 
                                               "Females in Services (%)")),
            size= 1.5, 
            show.legend = F)+
  theme(legend.position = "bottom")+
  labs( title = "Gender Workforce in High Income Countries")+
  scale_y_continuous(labels = function(x) paste0(x*1, "%"), 
                     breaks = seq(0,100,10), 
                     limits = c(0,100))
  
colegyemp

```


Workforce-Industry {data-icon="ion-ios-people"}
=================================================================================


Row {.sidebar data-width=300}
---------------------------------------------------------------------------------

**Gender Workforce by Industry**

*  Females have in all of them the **highest rate of employment in services as well as the lowest in agriculture**, this does not apply in Egypt but the current trend is showing that there is a moving out of that industry.


Column {column-width=40%}
-----------------------------------------------------------------------


### Gender Workforce by Industry


```{r allvars, fig.width=20, fig.height=10, echo=FALSE, warning=FALSE, message=FALSE}

byyear %>% ggplot(aes(x = `Year`,
                y = `Percentage`, 
                group = `Country Name`, 
                color = `Country Name`))+
  facet_wrap(~fct_relevel(`Series Name`, c("Females in Agriculture (%)", 
                                          "Females in Services (%)",
                                           "Females in Industry (%)",
                                          "Males in Agriculture (%)",
                                           "Males in Services (%)"
                                          )), 
             ncol = 3)+
  theme(legend.position = "bottom")+
  scale_y_continuous(labels = function(x) paste0(x*1, "%"), 
                     breaks = seq(0,100,10), 
                     limits = c(0,100))+
  geom_line(aes(linetype=`Series Name` %in%  c("Females in Agriculture (%)",
                                               "Females in Industry (%)", 
                                               "Females in Services (%)")),
            size= 1.5, 
            show.legend = F,
            alpha = 0.8)+
  geom_point(size=2)


```

Unemployed-Gender {data-icon="ion-android-globe"}
=================================================================================


Row {.sidebar data-width=300}
---------------------------------------------------------------------------------

**Unemployment by Gender and Country**

* The percentage of female unemployment is way *more in developing countries* than that of the *developed countries* like US and France. For instance, in **2015** the reported percentage of females unemployed in United States was just 5% where as it was 24 percentage in developing countries like Egypt.

* The **gap in participation rates between men and women is narrowing in developed countries but continues to widen in developing countries**, as we can observe that the percentage is almost equal for both males and females in US and France where as in Columbia and Egypt, the employment rate is more for men than women.

* Another interesting observation from the tables was seen that the **overall unemployment tread in the developing countries is more than that of developed countries**. The basic cause of this can be the deficiency of the availability of essential consumer goods, often called wage goods.


Column {data-height=900}
---------------------------------------------------------------------------------

```{r echo= FALSE, warning=FALSE, message=FALSE}

# Libraries
library(tidyverse)
library(ggplot2)
library(readr)
library(broom)
library(stringr)
library(patchwork)
library(kableExtra)
library(knitr)
library(bookdown)
library(plotly)
library(dplyr)
library(tidyr)

Genderstatistics <- read_csv("Data/Genderstatistics.csv")%>%
    select(-c('Series Code','Country Code')) %>%
  rename('2011' = '2011 [YR2011]',
         '2012' = '2012 [YR2012]', 
         '2013' = '2013 [YR2013]', 
         '2014' = '2014 [YR2014]', 
         '2015' = '2015 [YR2015]', 
         '2016' = '2016 [YR2016]', 
         '2017' = '2017 [YR2017]', 
         '2018' = '2018 [YR2018]', 
         '2019' = '2019 [YR2019]')

Gender_statistics <- Genderstatistics %>% 
  filter(str_sub(`Series Name`, 1,12) == "Unemployment")%>%
 mutate(`2019`= as.numeric(`2019`))%>%
  pivot_longer(cols = -c(`Country Name`,`Series Name`),
names_to = "Year",
values_to = "count") %>% pivot_wider(names_from = "Series Name",
values_from = "count")

countries <- c("Colombia", "United States", "France", "Egypt, Arab Rep.")

```


### Analysis on total unemployed males and females in developed countries

**United States & France**

```{r tabref, echo= FALSE,  warning=FALSE, message=FALSE}

developed <- Gender_statistics%>%
  filter(`Country Name` %in% c("United States", "France"))%>%
  select(`Country Name`, Year, `Unemployment, female (% of female labor force) (national estimate)`, `Unemployment, male (% of male labor force) (national estimate)` ) %>%
  group_by(`Country Name`, Year)%>%
  summarise(Unemployed_F = sum(`Unemployment, female (% of female labor force) (national estimate)`), Unemployed_M = sum(`Unemployment, male (% of male labor force) (national estimate)`))

 knitr::kable(developed, caption = "Unemployed percentage of males and females in Developed countries")%>%
  kable_classic_2(c("striped", "hover"), full_width = F, font_size = 25)%>%
   row_spec(14, bold = T, color = "white", background = "red")
```

Column {data-height=900}
---------------------------------------------------------------------------------

### Analysis on total unemployed males and females in developing countries

**Colombia & Egypt**

```{r tabref1, echo=FALSE, warning=FALSE, message=FALSE}
developing <- Gender_statistics%>%
  filter(`Country Name` %in% c("Colombia", "Egypt, Arab Rep."))%>%
  select(`Country Name`, Year, `Unemployment, female (% of female labor force) (national estimate)`, `Unemployment, male (% of male labor force) (national estimate)` ) %>%
  group_by(`Country Name`, Year)%>%
  summarise(Unemployed_F = sum(`Unemployment, female (% of female labor force) (national estimate)`), Unemployed_M = sum(`Unemployment, male (% of male labor force) (national estimate)`))

  knitr::kable(developing, caption = "Unemployed percentage of males and females in Developing countries")%>%
  kable_classic_2(c("striped", "hover"), full_width = F, font_size = 25)%>%
     row_spec(14, bold = T, color = "white", background = "red")
```

Qualifications {data-icon="ion-ribbon-b"}
=================================================================================

```{r echo= FALSE, warning=FALSE, message=FALSE}

# Libraries
library(tidyverse)
library(ggplot2)
library(readr)
library(broom)
library(stringr)
library(patchwork)
library(kableExtra)
library(knitr)
library(bookdown)
library(plotly)
library(dplyr)
library(tidyr)

Genderstatistics <- read_csv("Data/Genderstatistics.csv")%>%
    select(-c('Series Code','Country Code')) %>%
  rename('2011' = '2011 [YR2011]',
         '2012' = '2012 [YR2012]', 
         '2013' = '2013 [YR2013]', 
         '2014' = '2014 [YR2014]', 
         '2015' = '2015 [YR2015]', 
         '2016' = '2016 [YR2016]', 
         '2017' = '2017 [YR2017]', 
         '2018' = '2018 [YR2018]', 
         '2019' = '2019 [YR2019]')

Gender_statistics <- Genderstatistics %>% 
  filter(str_sub(`Series Name`, 1,12) == "Unemployment")%>%
 mutate(`2019`= as.numeric(`2019`))%>%
  pivot_longer(cols = -c(`Country Name`,`Series Name`),
names_to = "Year",
values_to = "count") %>% pivot_wider(names_from = "Series Name",
values_from = "count")

countries <- c("Colombia", "United States", "France", "Egypt, Arab Rep.")

```


Row {.sidebar data-width=400}
---------------------------------------------------------------------------------

**Advance Education**

* **Unemployment rates fall with higher educational attainment**. As shown in the figures, we can see that the overall percentage of unemployment is between 2% to 6% in developed countries and close to 10%  in developing countries. 

* Moreover the difference between the **ratio of males and females unemployed is also low with advanced education for all countries except Egypt**, where the difference is close to 15%.  Possible reasons for that include the high cost of childcare, the expectation that women carry out the majority of household responsibilities, negative attitudes toward women in the workplace, lack of mobility, legal barriers, persistent wage gaps, sexual harassment in the workplace, and poor enforcement of anti-discrimination laws.

**Basic Education** 

* It is interesting to see that the **percentage of unemployment of males is more than that of females for all the years in France**.

* Egypt shows that with just basic education the percentage of unemployment can decrease. The gap between males and females unemployment has gone down from 9% to almost 3% over the years. 

**Intermediate Education**

* Those with low educational attainment *intermediate education* are both less likely to be labor force participants and more likely to be unemployed. The greatest gender differences in unemployment rates are seen among adults with lower levels of education as shown in the above figures. The percent of female unemployment is close to 30% in females whereas its 11% for males

* Whereas in **developed countries even though the percentage of unemployment is high with intermediate education**, it's satisfying to see that it is almost **equal for both males and females**.


Column {.tabset}
---------------------------------------------------------------------------------
### Advanced Education


```{r include= FALSE, echo=FALSE, warning=FALSE, message=FALSE}

library(webshot)
```

```{r Advancedeved, fig.cap = "Unemployment with advanced education in developed countries", echo=FALSE, warning=FALSE, message=FALSE}

Advance_education_developed <- Gender_statistics%>%
  filter(`Country Name` %in% c("United States", "France"))%>%
  select(`Country Name`, Year, `Unemployment with advanced education, female (% of female labor force with advanced education)`, `Unemployment with advanced education, male (% of male labor force with advanced education)`)%>%
  rename (Female = `Unemployment with advanced education, female (% of female labor force with advanced education)`,
          Male = `Unemployment with advanced education, male (% of male labor force with advanced education)`) %>%

  pivot_longer(cols = c(Female, Male), names_to = "Advance_M_F", values_to = "Percentage")

p <- ggplot(Advance_education_developed, aes(x = Year, y = Percentage, fill = Advance_M_F)) +
  geom_bar(stat='identity', position = "dodge",show.legend = FALSE) + facet_wrap(~`Country Name`)+
  scale_y_continuous(limits = c(0,35))

fig <- ggplotly(p)

fig
```


```{r Advancedeving, fig.cap = "Unemployment with advanced education in developing countries", echo=FALSE, warning=FALSE, message=FALSE}

Advance_education_developing <- Gender_statistics%>%
  filter(`Country Name` %in% c("Colombia", "Egypt, Arab Rep."))%>%
  select(`Country Name`, Year, `Unemployment with advanced education, female (% of female labor force with advanced education)`, `Unemployment with advanced education, male (% of male labor force with advanced education)`) %>%

  rename (Female = `Unemployment with advanced education, female (% of female labor force with advanced education)`,
          Male = `Unemployment with advanced education, male (% of male labor force with advanced education)`) %>%

  pivot_longer(cols = c(Female, Male), names_to = "Advance_M_F", values_to = "Percentage")


q <- ggplot(Advance_education_developing, aes(x = Year, y = Percentage, fill = Advance_M_F)) +
  geom_bar(stat='identity', position = "dodge",show.legend = FALSE) + facet_wrap(~`Country Name`) +
  scale_y_continuous(limits = c(0,35))

fig <- ggplotly(q)

fig
```

### Intermediate Education

```{r Intermediatedeved, fig.cap = "Unemployment with intermediate education in developed countries", echo=FALSE, warning=FALSE, message=FALSE}

intermediate_education_developed <- Gender_statistics%>%
  filter(`Country Name` %in% c("United States", "France"))%>%
  select(`Country Name`, Year, `Unemployment with intermediate education, female (% of female labor force with intermediate education)`, `Unemployment with intermediate education, male (% of male labor force with intermediate education)`)%>%

   rename (Female = `Unemployment with intermediate education, female (% of female labor force with intermediate education)`, Male = `Unemployment with intermediate education, male (% of male labor force with intermediate education)`) %>%

  pivot_longer(cols = c(Female, Male), names_to = "Intermediate_M_F", values_to = "Percentage")

t <- ggplot(intermediate_education_developed, aes(x = Year, y = Percentage, fill = Intermediate_M_F)) +
  geom_bar(stat='identity', position = "dodge",show.legend = FALSE) + facet_wrap(~`Country Name`)+
  scale_y_continuous(limits = c(0,35))

fig <- ggplotly(t)

fig
```


```{r Intermediatedeving, fig.cap = "Unemployment with intermediate education in developing countries", echo=FALSE, warning=FALSE, message=FALSE}

intermediate_education_developing <- Gender_statistics%>%
  filter(`Country Name` %in% c("Colombia", "Egypt, Arab Rep."))%>%
  select(`Country Name`, Year, `Unemployment with intermediate education, female (% of female labor force with intermediate education)`, `Unemployment with intermediate education, male (% of male labor force with intermediate education)`) %>%

     rename (Female = `Unemployment with intermediate education, female (% of female labor force with intermediate education)`, Male = `Unemployment with intermediate education, male (% of male labor force with intermediate education)`) %>%

  pivot_longer(cols = c(Female, Male), names_to = "Intermediate_M_F", values_to = "Percentage")

u <- ggplot(intermediate_education_developing, aes(x = Year, y = Percentage, fill = Intermediate_M_F)) +
  geom_bar(stat='identity', position = "dodge",show.legend = FALSE) + facet_wrap(~`Country Name`)+
  scale_y_continuous(limits = c(0,35))


fig <- ggplotly(u)

fig
```

### Basic Education

```{r Basicdeved, fig.cap = "Unemployment with basic education in developed countries", echo=FALSE, warning=FALSE, message=FALSE}

basic_education_developed <- Gender_statistics%>%
  filter(`Country Name` %in% c("United States", "France"))%>%
  select(`Country Name`, Year, `Unemployment with basic education, female (% of female labor force with basic education)`, `Unemployment with basic education, male (% of male labor force with basic education)`)%>%

  rename (Female = `Unemployment with basic education, female (% of female labor force with basic education)`,
          Male = `Unemployment with basic education, male (% of male labor force with basic education)`) %>%

  pivot_longer(cols = c(Female, Male), names_to = "Basic_M_F", values_to = "Percentage")

r <- ggplot(basic_education_developed, aes(x = Year, y = Percentage, fill = Basic_M_F)) +
  geom_bar(stat='identity', position = "dodge",show.legend = FALSE) + facet_wrap(~`Country Name`)+
  scale_y_continuous(limits = c(0,35))


fig <- ggplotly(r)

fig

```


```{r Basicdeving, fig.cap = "Unemployment with basic education in developing countries", echo=FALSE, warning=FALSE, message=FALSE}

basic_education_developing <- Gender_statistics%>%
  filter(`Country Name` %in% c("Colombia", "Egypt, Arab Rep."))%>%
  select(`Country Name`, Year, `Unemployment with basic education, female (% of female labor force with basic education)`, `Unemployment with basic education, male (% of male labor force with basic education)`)%>%


  rename (Female = `Unemployment with basic education, female (% of female labor force with basic education)`,
          Male = `Unemployment with basic education, male (% of male labor force with basic education)`) %>%

  pivot_longer(cols = c(Female, Male), names_to = "Basic_M_F", values_to = "Percentage")

s <- ggplot(basic_education_developing, aes(x = Year, y = Percentage, fill = Basic_M_F)) +
  geom_bar(stat='identity', position = "dodge",show.legend = FALSE) + facet_wrap(~`Country Name`)+
  scale_y_continuous(limits = c(0,35))


fig <- ggplotly(s)

fig

```


Conclusions
=================================================================================

***

With the help of our analysis, we can conclude that even though the gap between males and females is reducing in developed countries in terms of employment, it is important that equal opportunities are given to the female gender in the developing countries as well. As with this many developing countries would also see their average annual GDP growth increase. 

***
ETC5513- Collaborative and reproducible practices and Github made the whole process of collaborating with colleagues much easier for us. This is the biggest advantages of using Git. Unlike other systems, where all our code stored in one place and changes need to be carefully made one at a time, Git made it easy for us to make changes and merge them with our main analysis. No matter how big or small the change is, with the help of new branch the changes were made smoothly without risking of breaking the whole report.

*Regardless of the time difference, with the help of GITHUB it was easy for us to collaborate and produce a reproducible report*

***
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***
Thank You!